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Machine Vision

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Traditional technology companies and startups are racing to combine machine vision with AI/ML, enabling it to “see” far more than just pixel data from sensors, and opening up new opportunities across a wide swath of applications, including automotive.

A machine vision system is a combination of software and hardware that can capture and process information in the form of digital pixels. These systems can analyze an image, and take certain actions based on how it is programmed and trained. A typical vision system consists of an image sensor (camera and lens), image and vision processing components (vision algorithm) and SoCs, and the network/communication components.

Both still and video digital cameras contain image sensors. So do automotive sensors such as lidar, radar, ultrasound, which deliver an image in digital pixel form, although not with the same resolution. While most people are familiar these types of images, a machine also can “see” can heat and audio signals data, and they can analyze that data to create a multi-dimensional image.

Machine vision is a subset of the broader computer vision. Generally speaking, MV can see better than people. The MV used in manufacturing can improve productivity and quality, lowering production costs. Paired with ADAS for autonomous driving, MV can take over some driving functions.

The benefits of using machine vision include higher reliability and consistency, along with greater precision and accuracy (depending on camera resolution). And unlike humans, machines do not get tired, provided they receive routine maintenance. Vision system data can be stored locally or in the cloud, then analyzed in real-time when needed. Additionally, MV reduces production costs by detecting and screening out defective parts, and increases inventory control efficiency with OCR and bar-code reading, resulting in lower overall manufacturing costs.

Today, machine vision usually is deployed in combination with AI, which greatly enhances the power of data analysis. With AI/ML, MV can self-learn and improve after capturing digital pixel data from sensors. This also can include deep learning techniques.

AI has an increasing number of roles in modern vehicles, but the two major roles are in perception and decision making. MV can detect uniform shapes such as squares or circles as well as odd-shaped parts, so it can be used to identify, detect, measure, count, and (with robots), pick and place. Combined with AI, MV can perform tire assembly with precision and efficiency. Nowadays, OEMs automate vehicle assembly with robots. One of the processes is to install the four wheels to a new vehicle. Using MV, a robotic arm can detect the correct distance and apply just the right amount of pressure to prevent any damage.

One challenge is making sure MV is secure. With cyberattacks increasing constantly, it will be important to ensure no production disruption or interference from threat actors. There are four key areas where security is essential in machine vision applications:

  • Data privacy. Machine vision systems often process large amounts of data, including sensitive personal or commercial information. It’s essential to protect this data from unauthorized access or disclosure. This can be achieved through encryption, access control, and data anonymization.
  • System integrity. Machine vision systems can be vulnerable to attacks that manipulate or disrupt their operation. It’s essential to protect the system components and data from tampering or hacking attempts. This can be achieved through secure boot, system hardening, and intrusion detection.
  • Authentication. Machine vision systems often rely on sensors, cameras, and other devices subject to spoofing or impersonation attacks. Ensuring these devices are authenticated is essential, and the system can detect and prevent unauthorized access. This can be achieved through biometric authentication, device certificates, and network segmentation.
  • Compliance. Machine vision systems may be subject to regulatory or industry-specific requirements related to security and privacy. Ensuring that the system design and operation comply with these requirements is essential. This can involve techniques such as risk assessment, audit trails, and data retention policies.

 

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